Introduction & Background: Breast cancer is a lead-ing cause of cancer deaths among women.Early de-tection is the most effective way to reduce mortality. Mammography is the best method for early detection. In order to improve the accuracy of interpretation of mammogram, Computer Aided Diagnosis (CAD) sys-tems have been proposed. The main goal of this re-search is to implement one of the algorithms and techniques for the enhancement of mammogram for easier detection of abnormalities. Patients & Methods: In the presented algorithm, morphological methods are used first for removing artifacts. Then thresholding, labeling, and active con-tours methods are used to extract the breast region, which allow the search for abnormalities to be lim-ited to the region of the breast. Finally, Gaussian fil-ter and White Top Hat Transform is used for contrast enhancement of mammogram. This algorithm has been applied on 50 images from Mammography Im-age Analysis Society (MIAS). An expert radiologist then verified improvement on the processed images. Results and Conclusions: Implementing the presented algorithm causes easier and better interpretation of mammogram without increasing the number of false positive and false negative results. Because of the spe-cial shapes and statistical features of abnormal tex-tures, it is possible to apply pattern recognition and artificial intelligence techniques as an aid for diagnos-ing suspicious regions. Research on using some of these techniques to distinguish benign abnormalities from malignant ones is on the way.